Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring

• URL: http://arxiv.org/abs/2002.02105v1
• Date: Thu, 6 Feb 2020 05:45:39 GMT
• Title: Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring
• Authors: Jingxiao Liu, Bingqing Chen, Siheng Chen, Mario Berges, Jacobo Bielak, HaeYoung Noh
• Abstract summary: We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle. Our feature provides the best damage and localization results across different bridges in five of six experiments.
• Score: 25.17078512102496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health. Motivated by indirect sensing methods' benefits, such as low-cost and low-maintenance, vehicle-vibration-based bridge health monitoring has been studied to efficiently monitor bridges in real-time. Yet applying this approach is challenging because 1) physics-based features extracted manually are generally not damage-sensitive, and 2) features from machine learning techniques are often not applicable to different bridges. Thus, we formulate a vehicle bridge interaction system model and find a physics-guided DS & DI feature, which can be extracted using the synchrosqueezed wavelet transform representing non-stationary signals as intrinsic-mode-type components. We validate the effectiveness of the proposed feature with simulated experiments. Compared to conventional time- and frequency-domain features, our feature provides the best damage quantification and localization results across different bridges in five of six experiments.

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